DataSift has built a top-of-the-line, engineer-friendly platform. It's more than just data streams.

Michael Muse, Co-Founder and VP of Product and Operations, LocalResponse

Case Study

Case Study Highlights

LocalResponse developed a unique methodology for measuring intent using social signals found in data from the Twitter Firehose.

To remain focused on refining their natural langauge processing technique LocalResponse turned to DataSift to access and filter through the full Twitter firehose to ingest only the data they need using powerful data enrichments and filters.

Using Twitter data enriched by DataSift, LocalResposne increased value for customers. In one campaign there were able to improve ad click through rates by 300%.

About

LocalResponse makes ads more relevant by listening to social conversations. Over 13 billion Tweets and other social media signals are analyzed monthly to improve the targeting and efficacy of desktop, tablet and mobile display ads. For example, if a person Tweets 'Just went for a run', they might see a Nike banner ad on their desktop minutes later.

Business Challenge

To identify real-time intent, Local Response needed to listen, filter, and process huge volumes of social data from Twitter in a highly scalable manner. In addition, the company needed to filter and cleanse the data with minimal back end post-processing effort.

Dealing with that size of unstructured data was a massive undertaking that the company wasn't equipped to manage and building out that capability would have distracted them from continuing to refine and add value to their core product.

As public intent data continues to grow in scale and complexity, DataSift helps us stay at the forefront of leveraging this data.

Michael Muse, Co-Founder and VP of Product and Operations, LocalResponse

DataSift Solution

After deploying DataSift in May of 2012, LocalResponse was able to access and filter through the full firehose of Tweets to ingest only the data they needed. They get additional context using enrichments like link expansion, klout score, language detection, sentiment and gender. DataSift's data enrichment and filters provides LocalResponse with the rich social insight they use to power their application.

LocalResponse also uses DataSift Historics to power their HIT (Historical Intent Targeting) solution, enabling them to mine past Twitter data to uncover new audiences that they would have never known existed.

DataSift has built a top-of-the-line, engineer-friendly platform. It's more than just data streams. The additional filters that DataSift adds on top are what really give us the Results power to get to exactly the data we need

Michael Muse, Co-Founder and VP of Product and Operations, LocalResponse

The Results

LocalResponse’s business expertise lies in using Natural Langauge Processing to uncover intent in social data and then delivering ads one-on-one against those who express the intent. DataSift has helped the company remain focused on these areas, while providing the critical supporting technology to deliver a highly enriched firehose of social data that feeds the LocalResponse solution. In doing so, DataSift helps LocalResponse provide their customers with more successful advertising campaigns.

For example, LocalResponse worked with a major media advertiser to run a campaign around the movie 'Sparkle' which featured Whitney Houston as one of the main actresses. Despite the lack of an official Whitney Houston Twitter account, Local Response identified her fans by running queries to search for those who had expressed sadness on Twitter after her death. The company then targeted that engaged audience with ads for the movie. This advertiser saw a 3X improvement in click-through rates based on LocalResponse's unique ability to better target the audience for this campaign.